Metastatic clear-cell renal cell carcinoma: a frequent NOTCH1 mutation predictive of response to anti-NOTCH1 CB-103 treatment

Background Clear-cell renal cell carcinomas (ccRCCs) are malignant tumors with high metastatic potential and resistance to treatments occurs almost constantly. Compared to primary tumors, there are still limited genomic data that has been obtained from metastatic samples. Methods We aimed to characterize metastatic ccRCC by way of whole-genome analyses of metastatic formalin-fixed samples, using OncoScan® technology. We identified a frequent, unexpected pL1575P NOTCH1 mutation which we set out to characterize for translational purposes. We thus implemented patient-derived xenografts from metastatic samples of human ccRCC to explore its clinical significance. Results We showed that pL1575P NOTCH1 mutation was an activating mutation, leading to the expression of NOTCH1-intracellular domain-active fragments in both cancer cells and tumor endothelial cells, suggesting a trans-differentiation of cancer cells into tumor micro-vessels. We demonstrated that this mutation could be used as a predictive biomarker of response to CB-103, a specific NOTCH1-intracellular domain inhibitor. One striking result was the considerable anti-angiogenic effect, coherent with the presence of NOTCH1 mutation in tumor micro-vessels. Conclusions We identified a frequent, unexpected pL1575P_c4724T_C NOTCH1 mutation as a new biomarker for ccRCC metastases, predictive of response to the CB103 NOTCH1-intracellular domain inhibitor. Supplementary Information The online version contains supplementary material available at 10.1186/s40164-023-00408-z.

A DNA obtained from a patient with lymphoblastic acute leukaemia T and the presence of pL1575P NOTCH1 mutation was provided by P.V and used as the positive control.
The systematic controls used were the absence of primary antibody and the use of an irrelevant primary antibody of the same isotype.

Immunofluorescence staining
To detect NOTCH1-ICD in the endothelial cells, a double indirect immunofluorescence method was performed using NOTCH1-ICD and CD31 as primary antibodies. Staining was performed with Tyramide detection kit 488CF (biotium, 99824), and 543CF (biotium, 99825) respectively, and a fluorescent mounting medium with DAPI was used for nucleus detection (E19-18, GBI labs).

Western-blot analysis
Protein extraction was performed from cryopreserved tissue with RIPA buffer (Thermo) supplemented with 1 EDTA-free protease inhibitor cocktail tablet (Roche Diagnostics) and phosphatase inhibitors (Sigma-Aldrich).

Laser micro-dissection of NOTCH1-ICD expressing cells
To confirm that NOCTH1-ICD expression in cancer cells or tumor endothelial cells was associated with NOTCH1 mutation, we combined laser micro-dissection of NOTCH1-ICD expressing cells with ddPCR for NOTCH1 mutation.
For each sample analysed, 7 µm-thick tissue sections were laser-micro-dissected to select a minimum of 300 cancer cells or 100 tumor endothelial cells expressing NOCTH1-ICD, using a PALM-Microbeam/Zeiss-system (Carl Zeiss, Germany). Total DNA was extracted from the micro-dissected cells using DNeasy-Micro-Kit (Qiagen, Courtaboeuf, France), and concentrated in a final volume of 10 μL.
For NOTCH1 gene copy number analyses, total DNA extracted from micro-dissected tumor cells was processed using ddPCR as described above.

Patient-derived clear-cell renal cell carcinoma xenografts and treatment
Five patient-derived clear-cell RCC xenograft models obtained from biopsies of metastases were implemented in our research unit [2] (Supplementary Table 6). The National Ethics Committee for experimental animal studies approved this study (APAFIS#17190-2018101814245111). The ethical guidelines that were followed met the standards required by the US guidelines [3]. Animals were maintained in pathogen-free housing at Sorbonne Paris Nord University (agreement number: C9300801).
After successful engraftment, tumor growth was measured in two perpendicular diameters with a caliper. Tumor volumes were calculated as follows: V = L × l2 ÷ 2, L being the larger diameter (length), l the smaller (width). When tumors reached a volume of 400 mm3 (n = 6 mice per treatment group), the mice were separated into four groups: i) treatment by gavage with sunitinib at 40 mg/kg/day (group 1), ii) treatment by gavage with NOTCH1 inhibitor (CB-103, HY-135145, MedChem Express at 25 mg/kg/twice daily or (LY411575, S2714, Selleckchem) at 10 mg/kg/day (group 2)); iii) combined treatment by gavage with sunitinib, at 40 mg/kg/day, and with NOTCH1 inhibitor (either CB-103 or LY411575) (group 3); iv) treatment with 100 µL of 0.9% NaCl as a control (group 4). All treatments were administered for 30 days. A daily clinical score was recorded and tumor growth was measured weekly  Fig. 7), using a ratio of the slopes (a and a') of the straight lines before and after treatment. In all xenograft models, the coefficient of inhibition for a drug was calculated as (a'-a)/a -a, being the slope of the curve before the start of treatment (Day 0) -and a' the slope of the curve between Day 0 and Day 30 of treatment. For a given drug combination, if the inhibition coefficient was negative, the tumor was considered sensitive to the drug. If, in contrast, it was positive, the tumor was considered resistant to this drug.
For each tumor section analyzed, proliferation and apoptotic cell counts were performed on five different fields at x400 magnification, using a ProvisAX70 microscope (Olympus, Tokyo) with a wide-field eyepiece number 26.5 providing a field size of 0.344mm2 at X400 magnification. Microscope images were captured using a ColorView-III digital camera, and analyzed using Olympus-SIS Cell F software. The percentage of positive cells in 100 cancer cells was determined, and results were expressed as means ± SEM. For micro-vessel density, CD31-positive micro-vessels were counted on ten different fields, at X400 magnification. The number of positive micro-vessels was related to the total number of micro-vessels in a given surface area studied. Results were expressed as means ± standard deviation.

Statistical analysis
Statistical analyses were performed using R Studio 1.3.1073 statistical software.
For analysis of the copy number variation derived from the result from Oncoscan® technology and our meta-analysis data, we used the copynumber package in R.
For counts of NOTCH1-expressing tumor cells, the mean ±SEM was calculated for each tumor sample (primary RCC, metastasis or tumor-xenograft), and represented in bar graphs.
Quantitative values were compared using Student's t-test (two-tailed), while proportions were compared using the Z-test. P values under 0.05 were considered significant. Gap filling with A/T or G/C nucleotides; c) Exonuclease selection for gap filled probes; d) Cleavage at site 1 for probe opening and inversion; e) Probe amplification and biotinylation; f) Cleavage at site 2 to release the tag sequence; f) Array hybridization followed by staining with phycoerythrin through the biotin-streptavidin interaction; g) Array scanning. Blue and gray colors indicate presence and absence of the phycoerythrin fluorescence signal respectively. Supplementary Fig. 7. Tumor growth inhibition coefficient in XRCC models. For a drug or a drug combination, the coefficient of inhibition is calculated as (a'-a)/a, a being the slope of the curve before the start of treatment (Day 0), and a' the slope of the curve between Day 0 and Day 30 of treatment. If this growth inhibition coefficient is found to be less than 0, the tumor is considered sensitive to the drug administered; if it is above 0, the tumor is considered resistant to the drug.